We found that phenotypic variability can be driven by the heterogenous expression of key components within signal transduction cascade. Such variability has practical importance when modeling how cells vary in their drug response. In particular, we found that the topology of signal transduction cascades explain why small-drug inhibitors of proximal signaling components (e.g. Src) acted digitally (i.e. in an all-or-none manner) while inhibitors against distal signaling components (e.g. Mek) acted analogously (i.e. in a continuous manner) We expanded our findings on the phenotypic variability of cell signaling with a new methodology (termed cell-to-cell variability analysis): such method relies on single-cell phospho-profiling of primary cells in preclinical and clinical settings to identify which biological components (receptor, kinase, phosphatase, transcription factor) is quantitatively limiting in terms of functional consequences. We illustrated the strength of this approach by showing how response to IL-2 and IL-7 are mutually exclusive within individual primary T cells. A computational model, based on biochemical modeling and Bayesian optimization, was introduced to test how sequestration of a shared but limited receptor chain could generate such flip-flop in cytokine signaling. This study provided a mechanistic explanation for the transition between effector and memory cells within an isogenic population of T cells (Cotari et al., Science Signaling, 2013). Concomitantly, we introduced and distributed a computer program (named ScatterSlice) that enables experimenters to analyze the cell-to-cell variability in their Flow Cytometry data (Cotari et al. Science Signaling, 2013). Such methodology has found applications in many clinical settings (Palomba et al., PLoS One, 2014; Kitano et al., Cancer Immunol Res, 2014).